With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past dec...

Recommender Systems helps users to find items of interest from a large number of available items. Collaborative Filtering is the commonly used technology for recommender systems. The role of recommender system in matrimonial sites is profile matching based on the preferences given by the users. The users of matrimonial sites have a problem of overloaded choices of partners. This is because the ...

with the rapid expansion of the information on the internet, recommender systems play an important role in terms of trade and research. recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. ...

the rapid growth of world wide web has affected the nature of interactions between customers and companies enormously. one significant consequence of this phenomenon is definitely the emergence and development of e-commerce websites and online stores all over the web. in spite of its great benefits, online shopping could turn into a complicated procedure from the customer point of view. in most...

There are two main types of recommender systems for e-commerce applications: content-based systems and automated collaborative filtering systems. We are interested in combining the best features of both approaches. In this paper, we investigate the possibility of using the k-means clustering algorithm as a basis for automatically generating content descriptions from the user transaction data th...

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that auto-evaluation codes during practice motivates students learn programming. Since POJs have greater number problems their repository, learners experience information overload. Recommender systems are a common solution Current recommender used e-learning platforms inadequate for POJ since r...

Spurred by the advances in collaborative filtering, by applications that form the core business of companies such as Amazon and Netflix, and indeed by incentives such as the famous Netflix Prize, research on recommender systems has become quite mature and sophisticated algorithms that enjoy high prediction accuracy have been developed [1]. Most of this research has been concerned with what we r...

The importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic comp...

This paper presents analytical outcomes of scientometric mapping of research work done on the important emerging area of ‘Recommender Systems. Research on ‘Recommender Systems’ started during last few years and within a short span of time has gained tremendous momentum. It is now considered as important emerging areas of research in computational sciences and related disciplines. We have analyz...